Abstract

We have developed a robust qualitative method for robot exploration,
mapping, and navigation in large-scale spatial environments.
Experiments with a simulated robot in a variety of complex 2D
environments have demonstrated that our qualitative method can build
an accurate map of a previously unknown environment in spite of
substantial random and systematic sensorimotor error.

Most current approaches to robot exploration and mapping analyze sensor
input to build a geometrically precise map of the environment, then
extract topological structure from the geometric description. Our
approach recognizes and exploits qualitative properties of large-scale
space before relatively error-prone geometrical properties.

[sensorimotor ↔ control] → topology → geometry

At the control level, distinctive places and distinctive travel edges
are identified based on the interaction between the robot's control
strategies, its sensorimotor system, and the world. A distinctive
place is defined as the local maximum of a distinctiveness measure
appropriate to its immediate neighborhood, and is found by a
hill-climbing control strategy. A distinctive travel edge, similarly,
is defined by a suitable measure and a path-following control
strategy. The topological network description is created by linking
the distinctive places and travel edges. Metrical information is then
incrementally assimilated into local geometric descriptions of places
and edges, and finally merged into a global geometric map.
Topological ambiguity arising from sensorily indistinguishable places
can be resolved at the topological level by the exploration strategy.
With this representation, successful navigation is not critically
dependent on the accuracy, or even the existence, of the geometrical
description.

We present examples demonstrating the process by which the robot
explores and builds a map of a complex environment, including the
effect of sensory errors. We also discuss new research directions
that are suggested by this approach.